Drug safety · 2026
Hierarchical Algorithms for Constructing Mother-Father-Infant Linkages Under Modern Privacy Constraints.
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Li Brian Meng-Hsun, Cheng Michael Chun-Yuan, Lai Edward Chia-Cheng
Аннотация
BACKGROUND: Post-marketing safety surveillance of medications used during pregnancy depends on accurate reconstruction of mother-father-infant triads in administrative healthcare data. However, global privacy regulations increasingly restrict the use of direct familial identifiers, and few parent-infant linkage algorithms have been validated against a reference standard.
OBJECTIVE: We aimed to develop and validate a hierarchical deterministic algorithm that constructs mother-infant and father-infant linkages from variables widely available in administrative healthcare data internationally.
METHODS: Using Taiwan's National Health Insurance Research Database and Birth Certificate Application database (2012-19), we constructed mother-infant and father-infant linkages through a five-step hierarchical mother-infant procedure and a single-step father-infant match, incorporating dependent-guarantor relationships, paternal demographic proxies, and sibling history. We validated the algorithm against the historical Maternal and Child Health Database, which provided a deterministic reference standard of 1,570,007 mother-infant and 1,510,578 father-infant pairs among 1,676,503 registered infants.
RESULTS: The algorithm identified 1,554,179 mother-infant pairs (positive predictive value 97.01%, 95% confidence interval [CI] 96.99-97.04; sensitivity 96.03%, 95% CI 96.00-96.06) and 1,036,980 father-infant pairs (positive predictive value 93.64%, 95% CI 93.59-93.69; sensitivity 64.28%, 95% CI 64.21-64.36). Mother-infant performance remained stable across the 8-year period, whereas paternal sensitivity showed a secular drift that we characterized to support recalibration in future birth cohorts.
CONCLUSIONS: This hierarchical framework offers a reproducible, privacy regulation-compatible template for constructing parent-infant linkages to support pharmacovigilance research on parental medication exposures. Operating characteristics should be re-validated when the framework is applied in each new setting.
OBJECTIVE: We aimed to develop and validate a hierarchical deterministic algorithm that constructs mother-infant and father-infant linkages from variables widely available in administrative healthcare data internationally.
METHODS: Using Taiwan's National Health Insurance Research Database and Birth Certificate Application database (2012-19), we constructed mother-infant and father-infant linkages through a five-step hierarchical mother-infant procedure and a single-step father-infant match, incorporating dependent-guarantor relationships, paternal demographic proxies, and sibling history. We validated the algorithm against the historical Maternal and Child Health Database, which provided a deterministic reference standard of 1,570,007 mother-infant and 1,510,578 father-infant pairs among 1,676,503 registered infants.
RESULTS: The algorithm identified 1,554,179 mother-infant pairs (positive predictive value 97.01%, 95% confidence interval [CI] 96.99-97.04; sensitivity 96.03%, 95% CI 96.00-96.06) and 1,036,980 father-infant pairs (positive predictive value 93.64%, 95% CI 93.59-93.69; sensitivity 64.28%, 95% CI 64.21-64.36). Mother-infant performance remained stable across the 8-year period, whereas paternal sensitivity showed a secular drift that we characterized to support recalibration in future birth cohorts.
CONCLUSIONS: This hierarchical framework offers a reproducible, privacy regulation-compatible template for constructing parent-infant linkages to support pharmacovigilance research on parental medication exposures. Operating characteristics should be re-validated when the framework is applied in each new setting.